Staff Data Reliability Engineer - Hybrid

The Hartford•Columbus, OH

3d•$127,600 - $191,400•Hybrid

About The Position

The Hartford is seeking a dedicated Data Reliability Engineer (DRE) to focus specifically on the integrity, quality, and availability of our data assets and pipelines. This role is a key partner to the SRE team, concentrating on the "data journey" layer. You will apply SRE principles to data pipelines, ensuring our data products are consistently trustworthy and reliable for all downstream consumers. This role will have a Hybrid work schedule, with the expectation of working in an office location (Hartford, CT; Chicago, IL; Columbus, OH; and Charlotte, NC) 3 days a week (Tuesday through Thursday).

Requirements

Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
Bachelors degree and 5+ year’s overall experience in an Infrastructure, Data or related technology organization with increasing responsibilities as a hands-on technologist.
3+ year experience in Data Engineering, Data Quality, or a specialized SRE role within an enterprise data environment.
Hands-on experience with data warehousing and data lake technologies, including Snowflake, and cloud environments (AWS/GCP).
Hands-on experience with ETL pipelines using SQL Server Integration Services (SSIS) and SQL Server Management Studio (SSMS).
Hands-on experience in pipeline development and support using technologies like Informatica, Python/Pyspark, and distributed compute (EMR/Hadoop).
Experience in designing and implementing data quality checks, data validation frameworks, and data governance standards.
Hands on experience in software or cloud engineering. Familiarity with cloud service providers and their core capabilities (compute, containers, databases, APIs etc.).
In depth and hands on experience with data observability concepts and tools for monitoring data in motion and at rest (e.g., Monte Carlo, Bigeye, Astro Observe, Datafold, custom solutions).
A strong understanding of the "data journey" and the impact of data issues on business outcomes.
Expertise implementing AIOps to monitor, manage and self-heal data pipelines, using machine learning principles for anomaly detection.
Experience with prompt engineering, implementing AWS or Google AI services, AI enabled automation for data quality, reliability and pipeline performance management.
Expertise defining and implementing of DataOps practices

Nice To Haves

Collaborate with application teams to support and enhance software solutions utilizing the .NET framework (C# or VB.NET) interacting with SQL backend architectures. Develop and maintain robust enterprise web applications using ASP.NET (4.5 and 4.6)

Responsibilities

Establish and enforce Data Service Level Objectives (SLOs) focused on data freshness, completeness, and accuracy across critical data products.
Implement advanced data observability tools to monitor the entire data journey—from ingestion to consumption—detecting data quality anomalies, schema drifts, and pipeline delays in real-time.
Collaborate with Data Engineering to embed reliability patterns into data pipelines built using Informatica, Python/Pyspark, and running on platforms like Amazon EMR/Hadoop, Informatica and cloud native services.
Automate data validation, data reprocessing, data backfilling, and other manual operational tasks within the data lifecycle to reduce toil and improve operational efficiency.
Lead the response and resolution for data-related incidents (e.g., corrupt data, delayed reporting), ensuring fast recovery and effective post-incident reviews (blameless post-mortems).
Develop and automate sophisticated, data-aware runbooks for common data pipeline failures, data quality issues, and data recovery scenarios.